# Exploring Personality-Aware Interactions in Salesperson Dialogue Agents

arXiv: https://arxiv.org/abs/2504.18058 · HTML: https://mlsanity.com/paper/2504.18058/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits](https://mlsanity.com/paper/2305.02547/) (2023)
- [Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://mlsanity.com/paper/2406.01171/) (2024)
- [A User Simulator for Task-Completion Dialogues](https://mlsanity.com/paper/1612.05688/) (2016)
- [User Modeling for Task Oriented Dialogues](https://mlsanity.com/paper/1811.04369/) (2018)
- [SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues](https://mlsanity.com/paper/2204.10591/) (2022)
- [Injecting Salesperson's Dialogue Strategies in Large Language Models with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2404.18564/) (2024)
- [StyEmp: Stylizing Empathetic Response Generation via Multi-Grained Prefix Encoder and Personality Reinforcement](https://mlsanity.com/paper/2408.02271/) (2024)

## Built on by (most cited first, 2 of 2)

- [Structured Personality Control and Adaptation for LLM Agents](https://mlsanity.com/paper/2601.10025/) (2026)
- [From Simulation to Strategy: Automating Personalized Interaction Planning for Conversational Agents](https://mlsanity.com/paper/2510.08621/) (2025)

## Abstract (arXiv)

The integration of dialogue agents into the sales domain requires a deep understanding of how these systems interact with users possessing diverse personas. This study explores the influence of user personas, defined using the Myers-Briggs Type Indicator (MBTI), on the interaction quality and performance of sales-oriented dialogue agents. Through large-scale testing and analysis, we assess the pre-trained agent's effectiveness, adaptability, and personalization capabilities across a wide range of MBTI-defined user types. Our findings reveal significant patterns in interaction dynamics, task completion rates, and dialogue naturalness, underscoring the future potential for dialogue agents to refine their strategies to better align with varying personality traits. This work not only provides actionable insights for building more adaptive and user-centric conversational systems in the sales domain but also contributes broadly to the field by releasing persona-defined user simulators. These simulators, unconstrained by domain, offer valuable tools for future research and demonstrate the potential for scaling personalized dialogue systems across diverse applications.